With the explosive growth of music data enabled by online distribution and sharing, finding relevant ways to organize and index music is useful for everyday users and for search engines and music providers. Music listeners often wish for a smart playlist generator based on their moods, feelings, or concepts expressed in the songs. Similarly, many users of search engines and smart personal assistants often query for music based on certain concepts or aspects. For example, a user planning for long automobile trip may want to retrieve songs that have embedded the concepts of travel, roads, and cars. Another user may want music related to rain to listen to on a rainy day.
Previous approaches to match songs and concepts, or other content items, have focused on a signal processing approach where acoustic properties of songs are analyzed and compared, or a metadata-based approach where metadata and annotations, such as genre, associated with the songs are analyzed and compared. However, these approaches are computationally expensive and do not accurately correlate songs with particular concepts or moods.